U.S. patent application number 16/087694 was filed with the patent office on 2019-04-11 for automated contextual determination of icd code relevance for ranking and efficient consumption.
This patent application is currently assigned to Koninklijke Philips N.V.. The applicant listed for this patent is KONINKLIJKE PHILIPS N.V.. Invention is credited to Paul Joseph Chang, Merlijn Sevenster, Amir Mohammad Tahmasebi Maraghoosh.
Application Number | 20190108175 16/087694 |
Document ID | / |
Family ID | 58547483 |
Filed Date | 2019-04-11 |
United States Patent
Application |
20190108175 |
Kind Code |
A1 |
Sevenster; Merlijn ; et
al. |
April 11, 2019 |
AUTOMATED CONTEXTUAL DETERMINATION OF ICD CODE RELEVANCE FOR
RANKING AND EFFICIENT CONSUMPTION
Abstract
A radiology workstation (24) includes at least one display
component (30, 32); at least one user input device (28); and at
least one microprocessor (26, 34) programmed to generate a
contextual ranking of clinical codes for a context received via the
at least one user input device (28) and to display information
pertaining to the contextual ranking on the display component (30,
32) of the radiology workstation (24). The contextual ranking is
computed by the microprocessor from (i) statistics of occurrences
of the clinical codes in radiology reports contained in a radiology
reports database (10) and satisfying the context and (ii)
statistics of the clinical codes in problem lists contained in a
problem lists database and satisfying the context.
Inventors: |
Sevenster; Merlijn;
(Haarlem, NL) ; Chang; Paul Joseph; (Chicago,
IL) ; Tahmasebi Maraghoosh; Amir Mohammad;
(Arlington, MA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
KONINKLIJKE PHILIPS N.V. |
EINDHOVEN |
|
NL |
|
|
Assignee: |
Koninklijke Philips N.V.
Eindhoven
IL
The University of Chicago
Chicago
|
Family ID: |
58547483 |
Appl. No.: |
16/087694 |
Filed: |
April 4, 2017 |
PCT Filed: |
April 4, 2017 |
PCT NO: |
PCT/EP2017/058006 |
371 Date: |
September 24, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62319923 |
Apr 8, 2016 |
|
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 40/289 20200101;
G06F 16/248 20190101; G06F 40/205 20200101; G16H 50/70 20180101;
G16H 10/20 20180101; G06F 16/2462 20190101; G16H 50/20 20180101;
G06F 16/24578 20190101; G16H 15/00 20180101 |
International
Class: |
G06F 16/2457 20060101
G06F016/2457; G06F 16/2458 20060101 G06F016/2458; G06F 16/248
20060101 G06F016/248; G06F 17/27 20060101 G06F017/27 |
Claims
1. A radiology workstation comprising: at least one display
component; at least one user input device; and at least one
microprocessor programmed to generate a contextual ranking of
clinical codes for a context received via the at least one user
input device and to display information pertaining to the
contextual ranking as a ranked list or table on the display
component of the radiology workstation; wherein the contextual
ranking is computed by the microprocessor from (i) statistics of
occurrences of the clinical codes in radiology reports contained in
a radiology reports database and satisfying the context and (ii)
statistics of the clinical codes in problem lists contained in a
problem lists database and satisfying the context.
2. The radiology workstation of claim 1 wherein the context is
received by receiving a selection of a radiology examination via
the at least one user input device wherein the context is a context
of the selected radiology examination.
3. The radiology workstation of claim 1 wherein the context
includes at least one of an anatomical section and an imaging
modality.
4. The radiology workstation of claim 1 wherein the contextual
ranking of a clinical code C is computed from statistics of
occurrences of the clinical codes in radiology reports contained in
a radiology reports database including at least: a number of
clinical history sections of radiology reports satisfying the
context from which the code C is extracted; a total number of
reports satisfying the context; a number of problem lists
satisfying the context that contain the code C; and a total number
of problem lists satisfying the context.
5. The radiology workstation of claim 1 wherein the microprocessor
is programmed to generate the statistics of occurrences of the
clinical codes in radiology reports by extracting the code C from
radiology reports by performing natural language processing to
identify phrases representing one or more clinical concepts
corresponding to the clinical code C.
6. The radiology workstation of claim 1 wherein the clinical codes
are International Statistical Classification of Diseases and
Related Health Problems (ICD) codes.
7. (canceled)
8. The radiology workstation of claim 1 wherein the microprocessor
is programmed to display information pertaining to the contextual
ranking on the display component of the radiology workstation by:
displaying a medical report satisfying the context on the display
component of the radiology workstation; and on the display
component, highlighting sentences or phrases of the medical report
from which clinical codes with high relevance were extracted.
9. A non-transitory computer readable medium carrying software to
control at least one processor to perform an image acquisition
method, the method including: generating a contextual ranking of
clinical codes for a context received via at least one user input
device of a radiology workstation; and displaying information
pertaining to the contextual ranking on a display component of the
radiology workstation; wherein the contextual ranking is generated
from (i) statistics of occurrences of the clinical codes in
radiology reports contained in a radiology reports database and
satisfying the context and (ii) statistics of the clinical codes in
problem lists contained in a problem lists database and satisfying
the context.
10. The non-transitory computer readable medium of claim 9, further
including: receiving the context by receiving a selection of a
radiology examination via the at least one user input device
wherein the context is a context of the selected radiology
examination.
11. The non-transitory computer readable medium of claim 9, wherein
the context includes at least one of an anatomical section and an
imaging modality.
12. The non-transitory computer readable medium of claim 9, further
including: generating the contextual ranking of a clinical code C
from statistics of occurrences of the clinical codes in radiology
reports contained in a radiology reports database including at
least: a number of clinical history sections of radiology reports
satisfying the context from which the code C is extracted; a total
number of reports satisfying the context; a number of problem lists
satisfying the context that contain the code C; and a total number
of problem lists satisfying the context.
13. The non-transitory computer readable medium of claim 9, further
including: generating the statistics of occurrences of the clinical
codes in radiology reports by extracting the code C from radiology
reports by performing natural language processing to identify
phrases representing one or more clinical concepts corresponding to
the clinical code C.
14. The non-transitory computer readable medium of claim 9, wherein
the clinical codes are International Statistical Classification of
Diseases and Related Health Problems (ICD) codes.
15. The non-transitory computer readable medium of claim 9, further
including: displaying the contextual ranking as a ranked list or
table on the display component of the radiology workstation.
16. The non-transitory computer readable medium of claim 9, further
including: displaying information pertaining to the contextual
ranking on the display component of the radiology workstation by:
displaying a medical report satisfying the context on the display
component of the radiology workstation; and on the display
component, highlighting sentences or phrases of the medical report
from which clinical codes with high relevance were extracted.
17. (canceled)
18. (canceled)
19. (canceled)
20. (canceled)
Description
FIELD
[0001] The following relates generally to the medical arts, the
medical database arts, medical imaging arts, and related arts.
BACKGROUND
[0002] A feature of some Electronic Medical Record (EMR) systems
(also known in the art by similar nomenclature such as Electronic
Health Record, EHR systems) is providing for an EMR problem list
(PL) that contains the patient's historical and current information
in the form of International Classification of Diseases 10 (ICD-10)
codes. This information may be valuable to a radiologist who is
reading an imaging examination of the patient. The imaging
examination may, for example, be a computed tomography (CT) imaging
examination, a magnetic resonance (MR) imaging examination, a
positron emission tomography (PET) imaging examination, a computed
radiography (CR) imaging examination, or so forth. However, the PL
for a patient can be lengthy and cumbersome to review as the
majority of codes may be irrelevant for image interpretation. In
practice, a radiologist may not consult the PL for the patient in
performing a medical imaging examination reading.
[0003] The following provides new and improved devices and methods
which overcome the foregoing problems and others.
SUMMARY
[0004] In accordance with one aspect, a radiology workstation
includes at least one display component; at least one user input
device; and at least one microprocessor programmed to generate a
contextual ranking of clinical codes for a context received via the
at least one user input device and to display information
pertaining to the contextual ranking on the display component of
the radiology workstation. The contextual ranking is computed by
the microprocessor from (i) statistics of occurrences of the
clinical codes in radiology reports contained in a radiology
reports database and satisfying the context and (ii) statistics of
the clinical codes in problem lists contained in a problem lists
database and satisfying the context.
[0005] In accordance with another aspect, a non-transitory computer
readable medium carrying software to control at least one processor
to perform an image acquisition method is provided. The method
includes: generating a contextual ranking of clinical codes for a
context received via at least one user input device of a radiology
workstation; and displaying information pertaining to the
contextual ranking on a display component of the radiology
workstation. The contextual ranking is generated from (i)
statistics of occurrences of the clinical codes in radiology
reports contained in a radiology reports database and satisfying
the context and (ii) statistics of the clinical codes in problem
lists contained in a problem lists database and satisfying the
context.
[0006] In accordance with another aspect, a radiology workstation
includes at least one display component and at least one user input
device. At least one microprocessor is programmed to: generate a
contextual ranking of clinical codes for a context received via the
at least one user input device and to display information
pertaining to the contextual ranking on the display component of
the radiology workstation; and generate statistics of occurrences
of the clinical codes in radiology reports by extracting a code C
from radiology reports by performing natural language processing to
identify phrases representing one or more clinical concepts
corresponding to the clinical code C. The contextual ranking is
computed by the microprocessor from (i) statistics of occurrences
of the clinical codes in radiology reports contained in a radiology
reports database and satisfying the context and (ii) statistics of
the clinical codes in problem lists contained in a problem lists
database and satisfying the context.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] The invention may take form in various components and
arrangements of components, and in various steps and arrangements
of steps. The drawings are only for purposes of illustrating the
preferred embodiments and are not to be construed as limiting the
invention.
[0008] FIG. 1 diagrammatically illustrates a radiology workstation
incorporating an embodiment of the disclosed clinical code
contextual relevance ranking.
[0009] FIGS. 2-8 present experimental data as described herein.
DETAILED DESCRIPTION
[0010] One possible approach for making the contents of the PL more
useful for a radiologist is to rank ICD codes by relevance to
medical imaging examination reading generally, or more particularly
by relevance to the particular imaging examination (e.g. CT, MR,
PET, . . . ) being performed. One approach is to provide (1) a rule
based approach that determines ICD code-specific relevance and/or
(2) a user interface device that lets the user provide feedback,
which is used to create and calibrate rules. For instance, with
regards to (1), it may assume that someone has entered the rule
indicating that all codes in the "Neoplasm" category have relevance
0.8. Regarding option (2), one possible scenario is that one or
more users have provided feedback in the workflow that all codes in
the "Benign Neoplasm" category is less relevant than its containing
category "Neoplasm". Both scenarios require manual input.
[0011] In illustrative approaches disclosed herein, relevance
scores are derived for ICD codes without human interaction. The
relevance scheme can be made contextual on various levels of
granularity, also without human intervention. The disclosed
approaches are based on the insight that mathematically, the
problem of determining relevance can be defined as the conditional
probability:
PROB(Code C is relevant|Code C is in problem list P) (1)
By definition, this is equivalent to:
PROB(Code C is relevant & Code C is in problem list
P)/PROB(Code C is in problem list P) (2)
Assuming that all relevant codes are contained in the problem list,
the numerator of Equation (2) is equal to:
PROB(Code C is relevant) (3)
Disclosed approaches for ranking codes in the PL by relevance are
based on the insight that both the numerator and the denominator
can be estimated by analysis of retrospective data. The probability
PROB(Code C is relevant) can be estimated as (# of clinical history
sections of radiology reports from which code C can be
extracted)/(# of reports). This estimation leverages typical
practice in which the radiologist includes in the clinical history
section clinical information that is relevant for the
interpretation. The probability PROB(Code C is in problem list P)
is estimated as (# of problem lists containing code C)/(# of
problem lists).
[0012] Various approaches are disclosed herein for estimating the
above two parameters from retrospective data, and for handling
codes that were not or rarely found in any clinical history and/or
problem list.
[0013] With reference to FIG. 1, some illustrative embodiments
include the following components: [0014] Radiology reports database
10--A database of radiology reports indexed with unique patient
identifiers and contextual parameters such as the imaged anatomical
section (e.g., abdomen/neuro/chest/angio/breast) and imaging
modality (e.g., CR/MR/CT/NIV1). [0015] Problem list (PL) database
12--A database of problem lists indexed with unique patient
identifiers. [0016] Natural language processing engine 14--A
natural language processing (NLP) engine that extracts codes from
particular fragments of narrative language. The codes are
controlled semantical entities from a background ontology, such as
ICD. [0017] Analytics engine 16--A statistical engine that
tabulates the relevant counts and aggregated them in a
code-specific relevance score. [0018] Escalation engine 18--A
hierarchical reasoning engine that determines the relevance of
codes based on the relevance of ancestor codes in the background
ontology (e.g., "Benign Neoplasm" has "Neoplasm" among its
ancestors). [0019] Persistence device 20--A method to persist the
automatically derived relevance scores and expose them to human
and/or automated agents.
[0020] A medical document viewer 22 may also be provided, which
serves as a viewer of medical documents that may, for example, have
"heat map functionality" for contextual radiological relevance.
[0021] In illustrative FIG. 1, the clinical code contextual
relevance ranking is shown in operative connection with a radiology
workstation 24 that provides a radiologist or other medical
professional with tools for performing and recording the reading of
a radiology examination (e.g. CT, MR, PET, CR, or so forth). The
illustrative radiology workstation 24 includes a computer 26 (with
a microprocessor, not shown), user input devices 28 (e.g.
illustrative keyboard, track pad, dictation microphone; additional
or other user input devices may be provided such as a mouse and/or
touch-sensitive display). The illustrative radiology workstation 24
further includes one or more (illustrative two) display devices or
components 30, 32, e.g. LCD displays. Preferably at least one of
these display components 30, 32 is a high resolution display for
displaying medical images. The radiology workstation 24 executes
suitable programming or software to enable the user to: retrieve
medical images from a Picture Archiving and Communication System
(PACS) 36 (also known in the art by similar nomenclatures such as a
Radiology Information System, RIS); display the images on the
display components 30, 32 preferably with the ability to perform
various image manipulations (zooming in/out, flipping through image
slices of a stack of image slices, producing three-dimensional
renderings, marking locations on an image and measuring distances
between markings, or so forth); prepare a text-based radiology
report (e.g. by typing and/or dictation, optionally with links to
medical images and/or inserted image thumbnails or
reduced-resolution embedded images or so forth). The illustrative
medical document viewer 22 is executed by suitable programming
executing on the radiology workstation 24 to display and permit
editing of such a radiology report (or, optionally more generally,
to present other types of medical reports).
[0022] With continuing reference to FIG. 1, some illustrative
embodiments of the components of the illustrative clinical code
contextual relevance ranking are described next. These components
(with the exception of the illustrative medical document viewer 22
implemented on the radiology workstation 24) are suitably
implemented on a server computer 34 or other computing system (e.g.
server cluster, cloud computing resource, et cetera, again each of
these inherently includes one or more microprocessors) programmed
by suitable software in accord with embodiments disclosed herein to
perform the disclosed processing. In other embodiments, if the
radiology workstation 24 has sufficient computing capacity then all
components including the components of the illustrative clinical
code contextual relevance ranking may be implemented locally on the
radiology workstation 24. Other partitioning of the various
processing amongst one, two, or more computers is also
contemplated.
[0023] The radiology reports database 10 comprises a database of
radiology reports, preferably stored using a standard (e.g.,
relational) database technology. The database can be obtained by
querying an existing database for radiology reports and pertinent
metadata, such as the Picture Archiving and Communication System
(PACS) 36.
[0024] The problem lists database 12 comprises a database of
problem lists, preferably stored using a standard (e.g.,
relational) database technology. The problem lists database 12 can
be obtained by querying an existing database for problem lists,
such as an Electronic Medical Record (EMR) 38. The patient
identifiers used in the problem lists database 12 should be
consistent with (e.g. the same as or capable of cross-referencing
with) the identifiers used in the radiology reports database 10.
This is generally the case in a typical setting such as a hospital
in which patient identifiers in the EMR 38 and PACS 36 should be
internally consistent.
[0025] The natural language processing (NLP) engine 14 operates to
extract concepts corresponding to clinical codes (for example,
ICD-10 codes) by natural language processing of radiology reports
from the radiology reports database 10. In one illustrative
example, this is done by: (1) detecting and normalizing section
headers in radiology reports, and (2) extracting concepts from one
or more fragments, such as the clinical history section. Step (1)
can be implemented using sentence boundary detection based on
string-matching or statistical techniques. Step (2) can be
implemented using concept extraction engines, such as MetaMap,
which is optimized for extracting SNOMED concepts. As an optional
step, concepts extracted from another ontology other than ICD, can
be mapped onto ICD using predetermined mapping tables--for example,
a mapping table for SNOMED to ICD is publicly available. Thus, the
NLP engine 14 detects phrases representing clinical concepts that
are identified in the SNOMED concepts database and thereby indexed
by SNOMED code, and the SNOMED code is then converted to an ICD-10
code using the SNOMED-to-ICD-10 mapping table. By combining
information provided by Steps (1) and (2), a given one or more
reports can be queried for the ICD codes residing in their clinical
history section.
[0026] It should be noted that while International Statistical
Classification of Diseases and Related Health Problems (ICD) codes
are used herein as the clinical code ontology, more generally the
approach can be used with any clinical code ontology. Similarly,
while ICD-10 codes are used herein, the ICD revision may be other
than the 10th revision.
[0027] An illustrative example of implementation of the analytics
engine 16 is next described. For a given ICD code C, the analytics
engine 16 tabulates the following parameters, based on querying the
radiology reports database 10, the problem list database 12 and the
NLP engine 14: [0028] # of clinical history sections from which C
was extracted by the natural language processing engine 14 [0029] #
of clinical history sections examined, i.e., # reports in the
radiology reports database 10 [0030] # of problem lists that
contain C [0031] # of problem lists examined, i.e., # problem lists
in problem list database 12 For an arbitrary ICD code C, the
relevance is then computed using the relevance score formulas:
[0031] PROB(Code C is relevant)=(# of clinical history sections of
radiology reports from which code C can be extracted)/(# of
reports) (4)
and
PROB(Code C is in problem list P)=(# of problem lists containing
code C)/(# of problem lists) (5)
The relevance of the code C is then computed from the results of
Equations (4) and (5) as:
PROB(Code C is relevant|Code C is in problem list P)=PROB(Code C is
relevant)/PROB(Code C is in problem list P) (6)
where Equation (6) follows from the insight described with
reference to Equations (1)-(3).
[0032] Using the metadata stored for each radiology report,
contextual relevance scores can be computed. For instance, if it is
desired to retrieve relevance scores for an abdomen study, the
analytics engine 16 derives the following counts: [0033] # of
clinical history sections from abdomen reports from which C was
extracted by the NLP engine 14 [0034] # of clinical history
sections examined from abdomen reports, i.e., # abdomen reports in
the radiology reports database 10 [0035] # of problem lists of
patients with a recent abdomen study (<1 yr) that contain C
[0036] # of problem lists of patients with a recent abdomen study
(<1 yr) examined, i.e., # problem lists of patients with a
recent abdomen study (<1 yr) in the problem list database 12
These values then serve as inputs to Equations (4)-(6) to compute
the contextual relevance score for the context of an abdomen study.
In the same way additional or other, optionally more granular,
contextual relevance scores can be facilitated, such as "abdomen
MR" study or "chest CR read by Dr. Doe" as long as the reports of
the radiology reports database 10 are appropriately indexed so that
the appropriate radiology reports can be identified.
[0037] With reference to FIG. 2, a worked example is presented for
ICD10 code I10 (Hypertension). As expected the code has highest
relevance score in the cardiac domain. In the example of FIG. 2, as
well as those described with reference to FIGS. 3-8, the following
data and analysis was employed. The code relevance metric of
Equations (4)-(6) was used to rank ordered ICD10 codes that are
more frequent in the history sections of radiology reports (thus
assumed relevant normalized by frequency in a set of PLs). To
determine code frequency, MetaMap detected code occurrences in a
de-identified corpus of 243,374 reports; PLs of 20,148 patients
were used for frequency normalization. The relevance metric was
contextualized for neuroimaging exams by filtering for neuro
reports and relevant PLs of patients with neuro exams. A similar
process was used for abdomen, musculoskeletal, cardiac and chest
examinations.
[0038] An illustrative embodiment of the escalation engine 18 is
next described. The rationale for including this optional component
is as follows. Counts aggregated by the analytics engine 16 are
based on observed frequencies. For some ICD codes, these observed
frequencies may be too small for meaningful analysis, in the sense
that the confidence interval of the two observed probabilities,
PROB(Code C is relevant) and PROB(Code C is in problem list), is
too wide. For instance, consider the following parameters: [0039] #
of problem lists that contain code C=1 [0040] # of problem lists
examined=1000 [0041] PROB(Code C is in problem list)=0.001 In this
event, the 95% confidence interval of PROB(Code C is in problem
list) ranges from 0.000025 to 0.006. If PROB(Code C is
relevant)=0.0005, then the confidence interval analysis indicates
that the relevance scores would range from 1 (=min{0.0005/0.000025,
1}) to 0.083 (0.0005/0.006).
[0042] The escalation engine 18 conducts a statistical analysis for
a given code and takes appropriate action if the outcome of the
statistical analysis indicates that the relevance score is not
trustworthy. Trustworthiness is determined is based on the type of
argument given above, or, in an alternative embodiment, by checking
counts against pre-determined thresholds, such as, for
instance:
# of problem lists that contain code C>T.sub.min (7)
where T.sub.min is some threshold value, e.g. T.sub.min=10 in some
contemplated embodiments. If the statistical analysis indicates
that the relevance score is not trustworthy, the escalation engine
18 iteratively seeks the relevance score of a code that is more
general than the current code until a trustworthy value is
obtained. In this analysis, the counts of the ancestor nodes are
cumulative with respect to the counts of their children and
grandchildren. This will result in higher counts, higher
probabilities, relatively smaller confidence intervals, and more
trustworthy relevance scores. The escalation engine 18 may leverage
the hierarchical tree-structures of ICD codes. This escalation
strategy is illustrated in FIG. 3.
[0043] This is merely one illustrative escalation approach.
Different escalation strategies can be implemented. For instance, a
default relevance score can be returned for codes that do not meet
the trustworthiness check, in which case there is effectively no
hierarchical escalation. In other implementation, each code is
always escalated up to a sufficiently general code level.
[0044] Some suitable embodiments of the persistence device 20 are
next described. The persistence device 20 exposes the relevance
scores to the user or to an application such as the illustrative
medical document viewer 22, for example as a table file that maps
individual ICD codes onto a relevance score, or as a digital object
for consumption by a problem list ranking method.
[0045] With reference to FIGS. 4-8, a further example is given. The
sample problem list (PL) is shown in FIG. 4 (under the tab
"Chronological"). In this table, the codes are ranked in reverse
chronological order ("newest to oldest"). The contextualized
rankings for abdomen, cardiac, chest and neuro are shown in FIGS.
5, 6, 7, and 8 respectively. These FIGS. 4-8 illustrate one
possible user interface approach, in which one of the tables of
FIGS. 4-8 are displayed on the display 30, 32 of the radiology
workstation 24 (see FIG. 1) with the user being able to select from
the upper graphical user interface (GUI) menu 50 any of: the
"Chronological" tab to bring up the table of FIG. 4; the "Abdomen"
tab to bring up the table of FIG. 5; the "Cardiac" tab to bring up
the table of FIG. 6; the "Chest" tab to bring up the table of FIG.
7; or the "Neuro" tab to bring up the table of FIG. 8. In this way,
the radiologist can quickly identify the most relevant ICD codes
(or, in FIGS. 4-8, their equivalent clinical meanings written in
natural language text) by clicking on the relevant context tab in
the upper menu 50 using the illustrative track pad or some other
pointing device 28 (e.g. mouse, trackball, touch-sensitive
screen).
[0046] In a contemplated variant embodiment, the radiology reports
database 10 and the problem list database 12 are configured such
that for each radiology report in the former database an image of
the then-current problem list is preserved. In this manner, it
would be possible to obtain the patient's problem list for each
time point of radiological interpretation. When configured in this
manner, the clinical codes ranking can be applied as already
described using the time history-appropriate problem list.
[0047] In another contemplated variant, the "latency" of an ICD
code is taken into account, defined as the duration of the interval
spanned by the time point the code was entered and the time point
of radiological interpretation (or "now"). Each ICD code extracted
from the clinical history section of a report, can be matched
against the then-current problem list and the code's latency can be
obtained. For each code, a time dependence curve can be created
indicating how the relevance of a code deteriorates over time. In
this manner, it can be established that fever is relevant if
reported within two years, but not more than that. Similarly, it
can be establishes that the relevance curve of malignant neoplasms
is stable, indicating that its relevance is not impacted by
time.
[0048] Some further approaches for leveraging the clinical code
rankings in the context of a radiology workstation 24 are next
described.
[0049] When the medical document viewer 22 is launched to open a
new radiology examination of a patient, the viewer 22 retrieves the
one or more medical documents from one or more medical repositories
(e.g. the EMR 38 or PACS 36) and applies an NLP engine 40 to them
to extract sentences and extract ICD codes. For each concept a
relevance score is calculated as already described. Sentences or
phrases from which codes were extracted with high relevance can be
highlighted in the report displayed on the display component 30, 32
of the radiology workstation 24, or can be presented separately. On
the other hand, a sentence from which codes were extracted with
only low relevance scores may be shown in a grayed-out or otherwise
de-emphasized format, or may be presented using ellipses (" . . .
") as placeholders with the "hidden" text being optionally selected
for display by the user clicking on (or hovering over, or otherwise
selecting) the ellipsis using a pointing device. The medical
document viewer 22 can further be configured to only
highlight/separate sentences from a select set of sections (e.g.,
impression and clinical history). These are merely illustrative
examples.
[0050] It will be further appreciated that the disclosed processing
may optionally be implemented as a non-transitory storage medium
storing instructions (e.g. a computer program) that is readable and
executable by a computer (e.g. the illustrative server 34 and/or
the computer 26 of the radiology workstation 24 of FIG. 1) to
perform the disclosed clinical code relevance ranking and
presentation operations. The non-transitory storage medium may, for
example, comprise a hard disk or other magnetic storage medium, an
optical disk or other optical storage medium, a solid state disk
drive or other electronic storage medium, various combinations
thereof, or so forth.
[0051] The invention has been described with reference to the
preferred embodiments. Modifications and alterations may occur to
others upon reading and understanding the preceding detailed
description. It is intended that the invention be construed as
including all such modifications and alterations insofar as they
come within the scope of the appended claims or the equivalents
thereof.
* * * * *